Automated Inspection of Closed Package Contents and Scanning to Optically Coupled Systems

ABSTRACT

Apparatus and associated methods relate to automatically generating a jurisdiction entry compliance indicator (JECI) based on an automatically generated content attributes profile (CAP) for a closed package. In an illustrative example, the CAP may be generated based on predetermined content attributes selected by a content characterization model as a function of a contents description profile (CDP) for the package. The CDP may, for example, be automatically generated by a natural language processing model as a function of a predetermined text description of contents of the package. The JECI may be generated as a function of the CAP, predetermined permissions rules, and predetermined permissions attributes. The predetermined permissions attributes may be associated with the originator. The predetermined permissions rules may be identified by a rules identification model as a function of the CAP and a destination of the package. Various embodiments may advantageously automatically inspect contents of closed packages.

TECHNICAL FIELD

Various embodiments relate generally to processing packages.

BACKGROUND

Packages may be transported through a distribution system. For example,a manufacturer may ship to a distributor. The distributor may ship toone or more other distributors. Packages may be shipped acrossjurisdictions (e.g., across national borders). Inter-jurisdictionshipments may be required to meet (predetermined) entry requirements.The entry requirements may, for example, be associated with a country oforigin and/or characteristics of items being shipped.

Packages may be tracked during shipment. For example, a shipper maytrack a package to verify the package arrived at an intended location. Acustomer may track a package to monitor an expected arrival time. Atransporter may track a package during transit to accomplish receipt,transit, and delivery as contracted.

SUMMARY

Apparatus and associated methods relate to automatically generating ajurisdiction entry compliance indicator (JECI) based on an automaticallygenerated content attributes profile (CAP) for a closed package. In anillustrative example, the CAP may be generated based on predeterminedcontent attributes selected by a content characterization model as afunction of a contents description profile (CDP) for the package. TheCDP may, for example, be automatically generated by a natural languageprocessing model as a function of a predetermined text description ofcontents of the package. The JECI may be generated as a function of theCAP, predetermined permissions rules, and predetermined permissionsattributes. The predetermined permissions attributes may be associatedwith the originator. The predetermined permissions rules may beidentified by a rules identification model as a function of the CAP anda destination of the package. Various embodiments may advantageouslyautomatically inspect contents of closed packages.

Various embodiments may achieve one or more advantages. For example,some embodiments may advantageously automatically inspect and/or rejecta package without requiring manual opening and/or inspection of thepackage. Various embodiments may, for example, advantageously provide asolution to problems associated with high labor costs, long processingtimes, and/or human variability (e.g., forgetting to apply one or morerules) associated with manual inspection. Embodiments may, for example,advantageously provide a technical solution to the technological problemof enabling a computer system to automatically inspect contents of aclosed package without opening the package (e.g., based on a textdescription of contents). Various such embodiments may, for example,advantageously provide a technical solution to the technological problemof enabling a computer system to automatically generate a visual indiciato pass, reject, and/or manually inspect a closed package based on apredetermined description of contents of the package (e.g., from a thirdparty). A number of packages to be physically opened may advantageouslybe drastically reduced.

Some embodiments may advantageously define content-based requirementsfor a package to be lawfully permitted to enter a destinationjurisdiction. Various embodiments may advantageously provide a technicalsolution to enabling a computer system to automatically determine, basedon contents of a closed package, whether the package can enter ajurisdiction, should be rejected for entry, and/or should be furtherinspected. Various embodiments may advantageously provide a technicalsolution to enable computers to communicate automatic content inspectionresults and/or resulting determined actions (e.g., pass, reject,inspect) to human operators.

Various embodiments may advantageously reduce time spent by humans ingenerating and/or reviewing forms to a fraction of the time spentmanually identifying and filling out information in customs forms

Various embodiments may advantageously enable a single scanningoperation to be used to provide input to multiple master systems.Embodiments implementing a scanner multiplier system may, by way ofexample and not limitation, advantageously (optically) connect otherwiseisolated (e.g., electronically isolated) data networks and/or systems.Various embodiments may advantageously allow a single scanning device toreport to multiple different (e.g., electronically isolated) mastersystems simultaneously. Such embodiments may advantageously reduce laborin scanning. Such embodiments may advantageously reduce energyexpenditure, handling costs, and/or laborer fatigue. Various suchembodiments may, for example, advantageously provide a technicalsolution to a technological problem of simultaneously verifying that apackage is scanned to multiple systems with a single scanning operation.Various embodiments may advantageously reduce a cost of equipment byallowing an inexpensive scanner to be used and optically connected toone or more higher cost ‘base’ scanners.

Various embodiments may, for example, advantageously enable adistributor to avoid duplication of scanning efforts by multiplying asingle scanning operation by a ‘slave’ scanner (scanning device 935)across a master controller 950 of the distributor's system and a masterscanning device 965 of the originator's system. Accordingly, theoriginator may advantageously avoid modifying their network orintroducing security vulnerabilities by using a physical scanning deviceapproved by the originator, while still receiving the benefit of reducedcosts and reduced processing time achieved by the distributor performinga single scanning operation per package. Such embodiments may, forexample, advantageously provide a technical solution to a technologicalproblem of physically scanning a package once when multiple physicalscanners must scan the same package. In various embodiments,discrepancies may be advantageously identified and rectified before datais scanned into a verification system.

The details of various embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an exemplary package content inspection system (PCIS)employed in an illustrative use-case scenario.

FIG. 2 depicts an exemplary block diagram of the exemplary PCIS.

FIG. 3 depicts an exemplary machine learning engine for contentcharacterization.

FIG. 4 depicts an exemplary machine learning engine for rulesidentification.

FIG. 5 depicts an exemplary method for automatic package contentinspection.

FIG. 6 depicts an exemplary method for automatic determination ofpredetermined permissions rules.

FIG. 7 depicts an exemplary method of training a contentcharacterization engine.

FIG. 8 depicts an exemplary method of training a rules identificationengine.

FIG. 9 depicts an exemplary scanner multiplier system (SMS) employed inan illustrative use-case scenario.

FIG. 10 depicts an exemplary block diagram of the exemplary SMS.

FIG. 11 depicts an exemplary method of scanner multiplication.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

To aid understanding, this document is organized as follows. First, tohelp introduce discussion of various embodiments, an automatic packageinspection (PI) system is introduced with reference to FIGS. 1-2 .Second, that introduction leads into a description with reference toFIGS. 3-4 of some exemplary embodiments of machine learning models usedin PI systems. Third, with reference to FIGS. 5-6 , exemplary methods ofautomatic package inspection are discussed. Fourth, with reference toFIGS. 7-8 , exemplary methods of training the machine learning modelsare described. Fifth, with reference to FIGS. 9-10 , the discussionturns to an introduction of a scanner multiplier system (SMS). Sixth,this disclosure discusses, with reference to FIG. 11 , an exemplarymethod for an SMS system. Finally, the document discusses furtherembodiments, exemplary applications and aspects relating to automaticpackage inspection and/or scanner multiplication.

FIG. 1 depicts an exemplary package content inspection system (PCIS)employed in an illustrative use-case scenario. In an exemplary scenario100, a package 105 is shipped from an originator in a first jurisdiction110 to a customs broker 115. The customs broker 115 applies a PCIS 120to a content description associated with a label 125 identifying thepackage 105 to determine how to handle the package 105. Based on theoutput of the PCIS 120, the customs broker 115 may be automaticallypassed to enter a destination jurisdiction 130 without manualinspection. In some examples, the output of the PCIS 120 may indicatethat the package 105 must be returned to the first jurisdiction 110. Insome examples, the output of the PCIS 120 may indicate that the package105 should be opened and the contents reviewed by an inspector 135.

In the depicted example, the PCIS 120 includes an automatic contentinspection engine (an ACIE 140). The ACIE 140 receives a (predetermined)description of contents corresponding to the label 125, and receives(e.g., retrieves) predetermined attributes and/or rules from a datastore 145. The ACIE 140 applies one or more models (e.g., naturallanguage processing, content characterization, rules identification)based on the description of the contents and generates a jurisdictionentry compliance indicator (JECI). Based on the JECI and predeterminedcriterion(s), an indication of how to handle the package is generated.The ACIE 140 may, as depicted, be operably coupled to a display 150 tocause a human-readable display indicating how to handle the package tobe automatically generated in response to receiving the description ofcontents. In the depicted example, the display 150 is configured todisplay a visual indication to pass 155 for the package 105. The display150 further includes, as depicted, a visual indication 160 of thedestination jurisdiction, the originator, and the contents of thepackage 105. Accordingly, various embodiments may advantageouslyautomatically inspect and/or reject a package without requiring manualopening and/or inspection of the package.

Various embodiments may, for example, advantageously provide a solutionto problems associated with high labor costs, long processing times,and/or human variability (e.g., forgetting to apply one or more rules)associated with manual inspection. Embodiments may, for example,advantageously provide a technical solution to the technological problemof enabling a computer system to automatically inspect contents of aclosed package without opening the package (e.g., based on a textdescription of contents). Various such embodiments may, for example,advantageously provide a technical solution to the technological problemof enabling a computer system to automatically generate a visual indiciato pass, reject, and/or manually inspect a closed package based on apredetermined description of contents of the package (e.g., from a thirdparty).

FIG. 2 depicts an exemplary block diagram of the exemplary PCIS. A PCIS200 (e.g., such as PCIS 120 disclosed at least with reference to FIG. 1) includes the ACIE 140 and the data store 145. The ACIE 140 includes aprocessor 205. The processor 205 may, for example, include one or moreprocessor. The processor 205 is operably coupled to a communicationmodule 210. The communication module 210 may, for example, include wiredcommunication. The communication module 210 may, for example, includewireless communication. In the depicted example, the communicationmodule 210 is operably coupled to at least one scanning device 215(e.g., barcode scanners, smartphones, tablets). In the depicted example,the communication module 210 is operably coupled to at least one display150 (e.g., an “inspection indication display,” as labeled in FIG. 2 ).

The processor 205 is operably coupled to a memory module 220. The memorymodule 220 may, for example, include one or more memory modules (e.g.,random-access memory (RAM)). The processor 205 includes a storage module225. The storage module 225 may, for example, include one or morestorage modules (e.g., non-volatile memory). In the depicted example,the storage module 225 includes a natural language processing model (anNLP engine 230). The NLP engine 230 may, for example, be applied to aninput signal (e.g., text input) to determine a content description inone or more predetermined languages.

The storage module 225 includes a content characterization engine 235.The content characterization engine 235 may, for example, be applied toan output of the NLP engine 230 and/or other data to determine(predetermined) attributes of content associated with a contentdescription.

The storage module 225 includes a rules identification engine 236. Therules identification engine 236 may, for example, be applied to anoutput of the content characterization engine 235 and/or other data todetermine (predetermined) permissions rules associated with a contentdescription, a destination jurisdiction, an originator, or somecombination thereof.

In some embodiments the NLP engine 230, the content characterizationengine 235, and/or the rules identification engine 236 may be combinedin a single model. For example, in some embodiments at least two of theNLP engine 230, the content characterization engine 235, and the rulesidentification engine 236 may be cascaded (e.g., in an ensemble model).In some embodiments, for example, at least two of the NLP engine 230,the content characterization engine 235, and the rules identificationengine 236 may be configured as a single model. For example, inputsdescribed for each model may be received and/or generated internally (ifneeded), and appropriate output(s) (e.g., final outputs) may begenerated. Such embodiments may, for example, reduce complexity ofoperation and/or maintenance. Such embodiments may, for example, enableenhanced associations (e.g., machine learning, statistical modeling)between descriptions, content attributes, predetermined rules, and/orhistorical data.

The processor 205 is further operably coupled to the data store 145. Thedata store 145, as depicted, includes package descriptions 240. Thepackage descriptions 240 may, for example, include digital data recordsstored in the data store 145 including descriptions of package contents.The descriptions may, for example, be textual. As an illustrativeexample, a package description may be generated for a customs manifest,such as “SOUTHPOLE BLUE JACKET MENS LG. $25.” Each of the packagedescriptions 240 may, for example, be associated with (e.g., include) aunique package identifier (PID).

The data store 145, as depicted, includes content description profiles245. For example, the content description profiles 245 may includedigital data records stored in the data store 145. The contentdescription profiles 245 may, for example, be generated by the processor205 using the NLP engine 230 as a function of one or more correspondingpackage descriptions 240. For example, the illustrative packagedescription may be translated into a predetermined language(s). Thepredetermined language may, for example, be determined as a function ofa destination jurisdiction. For example, if the destination jurisdictionis Mexico, and the label is in English, the package descriptions 240 maybe translated into Spanish. In some embodiments the NLP engine 230 may,for example, perform the translation. In some embodiments the NLP engine230 may operate on a translated package description(s).

As depicted, the data store 145 includes predetermined contentattributes 250. The predetermined content attributes 250 may, forexample, include digital data records stored in the data store 145. Thepredetermined content attributes may, for example, be associated withone or more jurisdiction-determined attributes. Carrying theillustrative example further, predetermined content attributes may, forexample, include clothing, men's clothing, adult clothing, outerwear,polymer fabric, natural fabric. The predetermined content attributesmay, for example, correspond to one or more harmonized tariff schedule(HTS). In some embodiments, for example, a predetermined contentattribute may include a predetermined association between apredetermined attribute (e.g., of a tangible item) and one or moreassociated classifications (e.g., in an HTS).

As depicted, the data store 145 includes content attributes profiles252. The profiles 252 may, for example, include digital data recordsstored in the data store 145. In some embodiments, such as depicted, theprofiles 252 may be generated by the processor 205 using the contentcharacterization engine 235 as a function of at least contentdescription profiles 245 and predetermined content attributes 250. Eachof the profiles 252 may, for example, include an association (e.g., viadigital metadata, via database entries) between at least one PID andcontent description profiles. Each of the predetermined contentattributes profiles 252 may, for example, include an association betweena PID and one or more corresponding predetermined content attributes250.

In the illustrative jacket example, a corresponding content attributeprofile may, for example, associate the package with attributes of“Adult Clothing,” “Outerwear,” “Nylon fabric,” and “Under 1000 pesos.”For example, the content characterization engine 235 may have determined(e.g., based on historical data and/or external data sources) that aSouthpole brand men's jacket is made of nylon. The contentcharacterization engine 235 may, for example, have determined that thedeclared value was $25 US Dollars (e.g., based on the originationjurisdiction and the text). The content characterization engine 235 may,for example, have determined that the declared value corresponded to avalue less than 1000 MXN (e.g., based on current exchange rates).

As depicted, the data store 145 includes predetermined permission rules255. The predetermined permission rules 255 may, for example, includedigital data records stored in the data store 145. The predeterminedpermission rules 255 may, for example, be associated with a destinationjurisdiction(s). In some embodiments predetermined permission rules 255may, for example, be associated with one or more content attributes(e.g., predetermined content attributes 250). In some embodiments,predetermined permission rules 255 may, for example, be associated withone or more HTSs. Predetermined permission rules 255 may, for example,be associated with one or more originators, origination locations,and/or origination jurisdictions.

As depicted, the data store 145 includes predetermined permissionattributes 260. The predetermined permission attributes 260 may, forexample, include digital data records stored in the data store 145. Thepredetermined permission attributes 260 may, for example, be generatedby the processor 205 using the rules identification engine 236. Each ofthe predetermined permission attributes 260 may, for example, include anassociation (e.g., via a digital metadata structure, via a databaserecord) between a profile 252 and one or more of the predeterminedpermission rules 255. Each of the predetermined permission attributes260 may, for example, include an association between a PID and one ormore of the predetermined permission rules 255. In some embodiments theassociation(s) may be determined, for example, based on a destinationjurisdiction of the corresponding PID. The association(s) may, forexample, be determined based on the predetermined content attributes 250in a specific profile 252. The predetermined permission attributes 260may, for example, advantageously define content-based requirements for apackage to be lawfully permitted to enter a destination jurisdiction.

The data store 145 includes predetermined confidence criterions 265. Thepredetermined confidence criterions 265 may, for example, includedigital data records stored in the data store 145. One or more of thepredetermined confidence criterions 265 may, for example, associate oneor more jurisdiction entry compliance indicators (JECIs) with apermission for a package to enter the destination jurisdiction. One ormore of the predetermined confidence criterions 265 may, for example,associate one or more JECIs with a rejection of a package entering thedestination jurisdiction. One or more of the predetermined confidencecriterions 265 may, for example, be associated with requiring furtherinspection (e.g., further inspection of package contents).

As an illustrative example, a first confidence threshold may beassociated with JECIs associated with a predetermined level ofconfidence sufficiently high to enter a jurisdiction without furtherinspection. A second confidence threshold may be associated with JECIsassociated with a predetermined level of confidence sufficiently low toreject entry into a jurisdiction without further inspection. A rangebetween the first and second confidence thresholds may, for example, beassociated with JECIs which are recommended for further inspection(e.g., manual opening and visual inspection of package contents,radiographic inspection, ultrasonic inspection). Accordingly, variousembodiments may advantageously provide a technical solution to enablinga computer system to automatically determine, based on contents of aclosed package, whether the package can enter a jurisdiction, should berejected for entry, and/or should be further inspected.

The data store 145 includes, in the depicted example, predeterminedindicia 270. Each of the indicia 270 may, by way of example and notlimitation, be associated with a predetermined outcome (e.g., pass,reject, inspect). The indicia 270 may, for example, each be associatedwith one or more predetermined confidence criterions 265. In someembodiments the indicia 270 may include visual indicia. Visual indiciamay, for example, include graphic elements (e.g., icons). Visual indiciamay, for example, include colors. One or more of the indicia 270 may,for example, include instructions such that the processor 205 may causea display device (e.g., display 150) to generate a human-readabledisplay of an indicia related to automatic inspection of a package.

As an illustrative example, green may be associated, by way of exampleand not limitation, with an automatic determination to pass the packagewithout further inspection. Red may, for example, be associated with anautomatic determination to reject the package without furtherinspection. Yellow may, for example, be associated with an automaticdetermination to perform further inspection of the package. In variousembodiments, various color schemas may be used. For example, the colorschemas may be pre-configured by an administrator. Accordingly, variousembodiments may advantageously provide a technical solution to enablecomputers to communicate automatic content inspection results and/orresulting determined actions (e.g., pass, reject, inspect) to humanoperators.

FIG. 3 depicts an exemplary machine learning engine for contentcharacterization. In an exemplary scenario 300, the contentcharacterization engine 235 includes a machine learning model. Themachine learning model may, by way of example and not limitation,include a neural network model. The neural network model may include,for example, recurrent neural network (RNN) and/or deep neural network(DNN). The machine learning model may, for example, include an ensemblemodel. Different neural network models may be selected. The number ofthe model layers (e.g., the hidden neurons) may also be determined basedon, for example, the complexity of content descriptions and/orattributes.

A set of training data is applied to the content characterization engine235 to train the machine learning model. The training data includes aset of training input data 305 and a set of training output data 310.The set of training input data 305 may include historical packagedescriptions 240. The training input data 305 may include, for example,historical content description profiles 245. The training input data 305may include, for example, current and/or historical predeterminedcontent attributes 250.

The set of training output data 310 may include historical contentattributes profiles 252. The training output data 310 may, for example,be selected to correspond to the training input data 305. As anillustrative example, historical content attributes profiles andhistorical content description profiles may be selected based on PIDs(e.g., matching PIDs). As an illustrative example, historical contentattributes profiles and predetermined content attributes may be selectedbased on time (e.g., content attributes available at a time the contentattributes profiles were generated).

In some embodiments, before training, a set of testing data (includingtesting input data and testing output data) may be divided from thetraining data. After the content characterization engine 235 is trained,the testing data may be applied to the trained model to test thetraining accuracy of the model. For example, the trained model mayreceive the testing input data and generate an output data in responseto the testing input data. The generated output data may be comparedwith the testing output data to determine the prediction accuracy (e.g.,based on a predetermined criterion(s) such as a maximum errorthreshold). In some embodiments, one or more models (e.g., neuralnetwork models) may be cascaded together. The cascaded model may betrained and tested.

During operation, a content description profiles 245 and a predeterminedcontent attributes 250 may be provided as inputs to the (trained)content characterization engine 235. The content characterization engine235 may generate, in response a corresponding content attributesprofile(s) 252.

FIG. 4 depicts an exemplary machine learning engine for rulesidentification. In an exemplary scenario 400, the contentcharacterization engine 235 includes a machine learning model. Themachine learning model may, by way of example and not limitation,include a neural network model. The neural network model may include,for example, recurrent neural network (RNN) and/or deep neural network(DNN). The machine learning model may, for example, include an ensemblemodel. Different neural network models may be selected. The number ofthe model layers (e.g., the hidden neurons) may also be determined basedon, for example, the complexity of content descriptions and/orattributes.

A set of training data is applied to the rules identification engine 236to train the machine learning model. The training data includes a set oftraining input data 405 and a set of training output data 410. The setof training input data 405 may include, by way of example and notlimitation, historical predetermined permission rules 255A (e.g., fromthe predetermined permission rules 255). The set of training input data405 may include, by way of example and not limitation, currentpredetermined permissions rules 255B (e.g., from the predeterminedpermission rules 255). The training input data 405 may include, forexample (historical) content attributes profiles 252.

The set of training output data 310 may include historical predeterminedpermission attributes 260. The training output data 310 may, forexample, be selected to correspond to the training input data 305. As anillustrative example, historical permissions attributes and historicalcontent attributes profiles may be selected based on PIDs (e.g.,matching PIDs). As an illustrative example, historical predeterminedpermissions rules may be selected based on time (e.g., predeterminedpermissions rules available at a time the permissions attributes weredetermined). As an illustrative example, historical and/or currentpredetermined permissions rules may be selected based on destinationjurisdiction (e.g., predetermined permissions rules corresponding to adestination jurisdiction(s) associated with the historic contentattributes profiles).

In some embodiments, before training, a set of testing data (includingtesting input data and testing output data) may be divided from thetraining data. After the rules identification engine 236 is trained, thetesting data may be applied to the trained model to test the trainingaccuracy of the model. For example, the trained model may receive thetesting input data and generate an output data in response to thetesting input data. The generated output data may be compared with thetesting output data to determine the prediction accuracy (e.g., based ona predetermined criterion(s) such as a maximum error threshold). In someembodiments, one or more models (e.g., neural network models) may becascaded together. The cascaded model may be trained and tested.

During operation, a profiles 252 and a predetermined permission rules255 (e.g., current predetermined permissions rules 255B) may be providedas inputs to the (trained) rules identification engine 236. The rulesidentification engine 236 may generate, in response to the input(suggested) predetermined permission attributes 260.

FIG. 5 depicts an exemplary method for automatic package contentinspection. A method 500 may, for example, be performed by aprocessor(s) (e.g., processor 205) executing a program(s) ofinstructions retrieved from a data store(s) (e.g., data store 145). Inthe method 500, a signal is received, in a step 505, identifying apackage (e.g., by a unique PID). The signal may, for example, bereceived in response to scanning a package (e.g., label 125 of thepackage 105). If it is determined, at a decision point 510, that thereceived signal includes a description (e.g., package descriptions 240)of the package contents, then an NLP model (e.g., NLP engine 230) isapplied, in a step 520, to the description. Otherwise, a predetermineddescription of contents (e.g., package descriptions 240) is retrieved,in a step 515 (e.g., based on the PID) and then the NLP model is appliedin the step 520. The description of contents may, for example, bepre-translated. The description of contents may, for example, betranslated by the NLP. A content description profile (CDP, e.g., contentdescription profiles 245) is generated, in a step 525, from the NLPmodel applied in the step 520.

A content characterization engine (CCE, e.g., content characterizationengine 235) is applied, in a step 530, to the CDP. The CCE generates, asan output, a content attributes profile (CAP, e.g., profiles 252) in astep 535. The CAP associates the package (e.g., the PID) at least withpredetermined content attributes (e.g., the predetermined contentattributes 250).

If it is determined, in a decision point 540, that the signal (receivedin the step 505) includes an indication of the originator anddestination of the package, then the method 500 proceeds to a step 550to determine a destination jurisdiction(s) corresponding to the packagedestination. Otherwise, a predetermined originator and/or destination isretrieved (e.g., based on the PID) in a step 545 and the method 500 thenproceeds to the step 550.

Predetermined permissions rules (e.g., predetermined content attributes250) are determined, in a step 555, based at least on the destinationjurisdiction and the CAP. For example, the predetermined permissionsrules may be identified and/or retrieved based on the destinationjurisdiction and the CAP. The predetermined permissions rules may, forexample, be identified using the rules identification engine 236.

In a step 560, predetermined permission attributes (e.g., predeterminedpermission attributes 260) are determined for the package based on theoriginator, the CAP, and/or the predetermined permission attributes. Insome embodiments, for example, the predetermined permission attributesmay be determined using the rules identification engine 236. Thepredetermined permission attributes may, for example, include permitsrequired to enter. The predetermined permission attributes may, forexample, include attributes associated with a simplified entry process(e.g., if the content attributes profiles met predetermined permissionsrules qualifying for a simplified process).

A JECI is generated, in a step 565, based on the predeterminedpermission attributes for the package and the correspondingpredetermined permission rules. The JECI may, for example, include ascore. The JECI may, for example, include a confidence interval ofcompliance of the package with the predetermined permissions rules basedon the predetermined permissions attributes. A predetermined confidencecriterion(s) (e.g., selected from the predetermined confidencecriterions 265) is applied to the JECI in a step 570.

In the depicted example, if a comparison of the criterion(s) to the JECIin a decision point 575, corresponds to an automatic decision to pass,an indication to permit the package to enter without opening it isgenerated (e.g., a human-readable visual indication(s)) in a step 580.If the comparison corresponds to an automatic decision to reject, anindication to refuse the package entrance without opening it isgenerated (e.g., a human-readable visual indication(s)) in a step 585.Otherwise, in the depicted example, an indication is generated (e.g., ahuman-readable indication(s)) is generated, in a step 590, indicatingthat the package should be further inspected (e.g., opened and inspectedby a human worker).

In some embodiments the indication to inspect may, by way of example andnot limitation, indicate a specific area of uncertainty. As anillustrative example, referring to the jacket example, if the contentsare determined to be a jacket, but the target age range is uncertain,the indication to inspect may prompt a human to ascertain, for example,whether the jacket is for a person greater than 3 years old (e.g.,corresponding to a predetermined permission rule associating additionalpermit requirements with articles for children ≤3 years). As anillustrative example, the indication to inspect may prompt a human toascertain, for example, whether the jacket contains natural fibers(e.g., corresponding to a predetermined permission rule associatingadditional permit requirements with articles made of natural fibers).

Subsequently, in a step 595, an inspection results signal(s) is received(e.g., after being prompted by the display generated in response to thestep 590) and one or more models are updated accordingly. For example,the NLP engine may be updated based on corrections to a description. TheCCE may be updated based on corrections to attributes (e.g., in a CAP)identified from the CDP. The rules identification engine may, forexample, be updated based on corrections to predetermined permissionattributes identified from the CAP.

In some embodiments, customs form(s) may be generated (step not shown)based, for example, at least on the predetermined permissionsattributes, predetermined permissions rules, and/or CAP. For example,the customs forms may be generated based on predetermined permitsassociated with the originator and the content attributes. Thepredetermined permits may, for example, correspond to predeterminedpermission attributes identified. The forms may be (automatically)populated using the CAP identified based on the predetermined permissionrules identified. Accordingly, various embodiments may advantageouslyreduce time spent by humans in generating and/or reviewing forms to afraction of the time spent manually identifying and filling outinformation in customs forms.

FIG. 6 depicts an exemplary method for automatic determination ofpredetermined permissions rules. A method 600 may, for example, beperformed by a processor(s) (e.g., processor 205) executing a program(s)of instructions retrieved from a data store(s) (e.g., data store 145).The method 600 includes, at a step 605, receiving a signal(s)corresponding to a content attributes profile (CAP), destinationjurisdiction(s), and originator for at least one package. The signal(s)may, for example, be received after the step 550 as disclosed at leastwith reference to FIG. 5 .

At a decision point 610, if express rules (e.g., simplified rules) aredetermined to be available (e.g., based on the destinationjurisdiction(s)), then the simplified rules are retrieved aspredetermined permissions rules in a step 615, and the method 600proceeds to a step 695. As an illustrative example, express rules mayinclude simplified rules (such as Mexico's T1 entry process for packagesunder a maximum declared value threshold).

If express rules are determined, in the decision point 610, to not beavailable, then historical data (e.g., in one or more databases) issearched, in a step 620, for unique item identifiers (UIDs). The UIDsmay, for example, be specified in the CAP(s). The UIDs may, for example,be declared in the CDP and/or may be determined based on the CDP andhistorical CAPs. If the UID(s) are determined to be found, in a decisionpoint 625, then historical permissions rules (e.g., historicalpredetermined permission rules 255A) associated with the UID(s) areretrieved (e.g., via historical predetermined permission attributes 260)in a step 630. The historical rules are compared, in a step 635, tocorresponding current rules (e.g., the current predetermined permissionsrules 255B). The current rules may be determined to correspond, forexample, based on a corresponding HTS. If changes are determined, in adecision point 640, to exist between the historical permissions rulesand the corresponding current rules, then the rules are updated in adatabase in a step 645, and the method 600 proceeds to a step 670.

If it is determined, in the decision point 625, that the UID is notfound in the historical data, then a rule identification engine (RIE) isapplied, in a step 650, to the CAP and the historical data to findsimilar historical contents. If it is determined, in a decision point655, that similar records are not found, then the method 600 proceeds tothe step 670. Otherwise, it is determined, in a step 660, whetherhistorical rules retrieved based on similar historical records areapplicable to the package contents (e.g., based on the CAP). In someembodiments the rules may be automatically reviewed based onpredetermined criterion (e.g., statistical similarity criteria). In someembodiments the rules may be (visually) presented to a (human)reviewer(s) for verification of applicability.

If the historical rules are determined to apply to the package contents,in a decision point 665, then the method 600 proceeds to a step 690.Otherwise, the method 600 proceeds to the step 670.

In the step 670, the RIE is applied to a current rules database (e.g.,predetermined permission rules 255) and the CAP to identify relatedcurrent rules. In a step 675, selected rules are determined based onpredetermined criterion(s). For example, the predetermined criterion(s)may include predetermined confidence criterions 265. A display of theselected rules is then generated, in a step 680. The display may, forexample, be a human-readable display (e.g., on the display 150). Thedisplay may include a prompt for verification of the selected rules(e.g., from a human reviewer).

If it is determined, in a decision point 685, that an input signal isreceived corresponding to verification of the selected rules (e.g., forapplicability to the package), then the method 600 proceeds to the step690. If it is determined, in the decision point 685, that an inputsignal is received corresponding to a rejection of the rules and/orupdated criteria (e.g., filters, weighting modifications), then themethod 600 returns to step 670 to apply the RIE based on the inputreceived from the reviewer. If it is determined, in the decision point685, that an input signal is received corresponding to a selection ofsome of the suggested rules selected in the step 675 and presented inthe step 680, then the set of selected rules is modified, in a step 688,based on the input from the reviewer, and the method 600 proceeds to thestep 690.

In the step 690, the RIE is updated (e.g., as disclosed at least withreference to FIG. 4 and FIG. 8 ) and the historical data (e.g.,associations between content attributes and predetermined permissionsrules) is updated. A data record is generated, in the step 695,associating the predetermined permissions rules to the package (e.g.,based on the PID), and the method 600 ends.

FIG. 7 depicts an exemplary method of training a contentcharacterization engine. A method 700 may, for example, be performed bya processor(s) (e.g., processor 205) executing a program(s) ofinstructions retrieved from a data store(s) (e.g., data store 145). Themethod 700 includes, at a step 705, receiving the historical contentdescription data (e.g., historical content description profiles 245). Ata step 710, corresponding content attributes (e.g., historic contentattributes profiles 252) are determined and retrieved. Predeterminedcontent attributes (e.g., predetermined content attributes 250) areretrieved in a step 715.

At a step 720, the retrieved data is divided into a first set of dataused for training and a second set of data used for testing. At a step725, a model (e.g., a model(s) of the content characterization engine235) is applied to the training data to generate a trained model (e.g.,neural network model). The trained model is applied to the testing data,in a step 730, to generate test output(s) (e.g., content attributeprofile(s)). The output is evaluated, in a decision point 735, todetermine whether the model is successfully trained (e.g., by comparisonto a predetermined training criterion(s)). The predetermined trainingcriterion(s) may, for example, be a maximum error threshold. Forexample, if a difference between the actual output (the test data) andthe predicted output (the test output) is within a predetermined range,then the model may be regarded as successfully trained. If thedifference is not within the predetermined range, then the model may beregarded as not successfully trained. At a step 740, the processor maygenerate a signal(s) requesting additional training data, and the method700 loops back to step 730. If the model is determined, at the decisionpoint 735, to be successfully trained, then the trained model may bestored (e.g., in the storage module 225), in a step 745, and the method700 ends.

FIG. 8 depicts an exemplary method of training a rules identificationengine. A method 800 may, for example, be performed by a processor(s)(e.g., processor 205) executing a program(s) of instructions retrievedfrom a data store(s) (e.g., data store 145). The method 800 includes, ata step 805, receiving the historical content attributes (e.g., historiccontent attributes profiles 252). At a step 810, corresponding(historical) predetermined permissions rules (e.g., historicalpredetermined permission rules 255A and/or current predeterminedpermissions rules 255B) are determined and retrieved.

At a step 815, the retrieved data is divided into a first set of dataused for training and a second set of data used for testing. At a step820, a model (e.g., a model(s) of the rules identification engine 236)is applied to the training data to generate a trained model (e.g.,neural network model). The trained model is applied to the testing data,in a step 825, to generate test output(s) (e.g., content attributeprofile(s)). The output is evaluated, in a decision point 830, todetermine whether the model is successfully trained (e.g., by comparisonto a predetermined training criterion(s)). The predetermined trainingcriterion(s) may, for example, be a maximum error threshold. Forexample, if a difference between the actual output (the test data) andthe predicted output (the test output) is within a predetermined range,then the model may be regarded as successfully trained. If thedifference is not within the predetermined range, then the model may beregarded as not successfully trained. At a step 835, the processor maygenerate a signal(s) requesting additional training data, and the method800 loops back to step 825. If the model is determined, at the decisionpoint 830, to be successfully trained, then the trained model may bestored (e.g., in the storage module 225), in a step 840, and the method800 ends.

FIG. 9 depicts an exemplary scanner multiplier system (SMS) employed inan illustrative use-case scenario. In an exemplary scenario 900, apackage 905 is transported from an originator 910 to a distributionfacility 915. The distribution facility 915 may, for example, determinea transport 920 to load the package 905 onto for further delivery basedon details associated with the package 905.

In the depicted example, the distribution facility 915 operates an SMS925. The SMS 925 optically connects a first scanning system associatedwith a (data) network of the distribution facility 915 with a secondscanning system associated with a (data) network of the originator 910.The SMS 925 may, by way of example and not limitation, advantageously(optically) connect otherwise isolated (e.g., electronically isolated)data networks and/or systems.

As depicted, the package 905 includes a label 930. The label 930 may,for example, include a code. The code may, for example, be opticallyreadable (e.g., a barcode, QR code). A scanning device 935 (e.g.,smartphone, barcode reader) may (optically) scan the label 930 andgenerate a signal corresponding to the code defined by the label 930.The scanning device 935 is coupled by a wireless link 940 to an opticalmulti-master scanning system 945 of the SMS 925. The scanning device 935may, for example, transmit a signal containing the code identifying thepackage 905 to a controller 950 of the optical multi-master scanningsystem 945. The controller 950 is operably coupled to a database 955,which may store the code. For example, the controller 950 and thedatabase 955 may be used to generate forms (e.g., customs broker forms)based on data corresponding to the package 905. The database 955 may,for example, be used to store a record of packages scanned in thedistribution facility 915.

The controller 950 is further operably coupled to a display device 960.The 950 may generate and transmit signal(s) to the display device 960corresponding to the code(s) received from the scanning device 935. Thesignal(s) to the display device 960 may operate the display device 960to generate a (visual) display of the code (e.g., a QR code as shown, abarcode).

A scanning device 965 may, for example, be configured to scan thedisplay device 960. For example, the scanning device 965 may bemechanically mounted to monitor the display device 960.

The scanning device 965 may be operated (e.g., internally, externally)to repeatedly scan the display device 960. In some embodiments, forexample, the scanning device 965 may scan the display device 960 at apredetermined time interval. The display device 960 may, for example,generate a display of a next available scanned code at the predeterminedtime interval (as available). For example, codes scanned by the scanningdevice 935 may be stored in a cache, for example, and opticallypresented on the display device 960 (e.g., in a predetermined order) tothe scanning device 965.

In some embodiments, the display device 960 may be operated by atrigger. For example, the controller 950 and/or the display device 960may generate a trigger signal when a new code is displayed on thedisplay device 960. The trigger signal may be transmitted to thescanning device 965 to cause the scanning device 965 to scan the displaydevice 960.

The scanning device 965, as depicted, is operably coupled to anoriginator network 970. The originator network 970 may, for example,receive the codes as scanned by the 965. The originator network 970includes, in the depicted example, a controller 975. The scanning device965 is operably coupled to the controller 975. The controller 975 isoperably coupled to a database 980. Accordingly, the scanning device 965may transmit the code(s) presented on the display device 960 to thecontroller 975 (e.g., for storage in the database 980).

Various embodiments may advantageously allow a single scanning device935 to report to multiple different (e.g., electronically isolated)master systems simultaneously. Such embodiments may advantageouslyreduce labor in scanning (e.g., scanning into the optical multi-masterscanning system 945 and scanning separately into the originator network970). Accordingly, such embodiments may advantageously reduce energyexpenditure, handling costs, and/or laborer fatigue. Such embodimentsmay advantageously provide a technical solution to a technologicalproblem of simultaneously verifying that a package is scanned tomultiple systems with a single scanning operation. Various embodimentsmay advantageously reduce a cost of equipment by allowing an inexpensivescanner to be used and optically connected to one or more higher cost‘base’ scanners.

FIG. 10 depicts an exemplary block diagram of the exemplary SMS. In adepicted exemplary system 1000, the optical multi-master scanning system945 includes the controller 950 and the database 955. The controller 950includes a processor 1005. The processor 1005 may, for example, includeone or more processors. The processor 1005 is operably coupled to amemory module 1010 (e.g., one or more random-access memory modules). Theprocessor 1005 is operably coupled to a communication module 1015. Thecommunication module 1015 may, for example, include wirelesscommunication. The communication module 1015 may, for example, includewired communication.

As depicted, the processor 1005 is operably coupled, via thecommunication module 1015, to the scanning device 935 and the controller975. The processor 1005 may, for example, receive manifest data 1025(e.g., a file such as a tabulated data, database package, XML file,metadata) from an originator delivery system 1020 of the controller 975.The processor 1005 may store the manifest data 1025 in the database 955.The manifest may, for example, include a series of package identifiers(PIDs) and associated package information (e.g., destination, contentsdescription, originator). In some embodiments the originator deliverysystem 1020 may not be operably coupled to the communication module1015. For example, the shipping manifest may be scanned, emailed,uploaded and/or otherwise stored into the database 955.

The processor 1005 may receive a code from the scanning device 935(e.g., scanning a package associated with the manifest data 1025). Theprocessor 1005 may store the code in a code cache 1050 (e.g.,random-access and/or non-volatile memory). The processor 1005 mayretrieve corresponding data from the manifest data 1025 to identify thepackage associated with the code. For example, the processor 1005 maystore associated scanned package data 1035. The associated scannedpackage data 1035 may, for example, include data retrieved from themanifest data 1025 for the package. The associated scanned package data1035 may include, for example, scanning information (e.g., size, weight,time, associated personnel). The associated scanned package data 1035may, for example, include content descriptions, originator, and/ordestination associated with the package (e.g., retrieved from themanifest data 1025).

The processor 1005 may, for example, further generate, receive, and/orstore outgoing shipment data 1040 associated with the package. Forexample, the outgoing shipment data 1040 may include a shipping route(e.g., corresponding to a transport 920) associated with the package.The outgoing shipment data 1040 may, for example, include a location ofthe package in a shipping carton and/or transport (e.g., truckidentifier, carton identifier). The outgoing shipment data 1040 may, forexample, include permission, permit, and/or customs data associated withthe package (e.g., as disclosed at least with reference to FIGS. 1-8 ).

The processor 1005 is operably coupled to a code generation engine 1045.The code generation engine 1045 may, for example, generate a code inresponse to a signal corresponding to a code. For example, the codegeneration engine 1045 may receive a PID (e.g., stored in the code cache1050) and generate a corresponding code according to a predeterminedstandard (e.g., QR code, barcode).

The processor 1005 is operably coupled to the display device(s) 960. Theprocessor 1005 may generate one or more signals, based on codes in thecode cache 1050 (e.g., received from the scanning device 935, processedby the code generation engine 1045), to the display device 960. In someembodiments, by way of example and not limitation, the processor 1005may generate the signal(s) in response to a timer, a status of the codecache 1050, a signal received from the scanning device 935, or somecombination thereof. The signal(s) to the display device 960 may causethe display device 960 to display a corresponding code from the codecache 1050 (e.g., previously received from the scanning device 935). Thescanning device 965 may (optically) scan the display device 960 andgenerate a signal corresponding to the code (visually) displayed on thedisplay device 960.

The scanning device 965 may transmit the generated signal(s) to anoriginator verification system 1055 of the controller 975. Theoriginator verification system 1055 is operably coupled to the database980. For example, the originator verification system 1055 may check thecode received from the scanning device 965 against a record stored inthe database 980 by the originator delivery system 1020 (e.g., manifestdata corresponding to manifest data stored in the manifest data 1025).Accordingly, the controller 975 may verify that the package(s) wasphysically scanned by the distributor. The distributor mayadvantageously avoid duplication of scanning efforts by multiplying asingle scanning operation by a ‘slave’ scanner (scanning device 935)across a master controller 950 of the distributor's system and a masterscanning device 965 of the originator's system. Accordingly, theoriginator may advantageously avoid modifying their network orintroducing security vulnerabilities by using a physical scanning deviceapproved by the originator, while still receiving the benefit of reducedcosts and reduced processing time achieved by the distributor performinga single scanning operation per package. Such embodiments may, forexample, advantageously provide a technical solution to a technologicalproblem of physically scanning a package once when multiple physicalscanners must scan the same package.

In the depicted example, the arrows between the scanning device 965 andthe originator verification system 1055 and between the originatordelivery system 1020 and the communication module 1015 may, at least insome embodiments, indicate one way communication (e.g., only).

FIG. 11 depicts an exemplary method of scanner multiplication. A method1100 may, for example, be performed by a processor(s) (e.g., processor1005) executing a program(s) of instructions retrieved from a datastore(s) (e.g., database 955, code generation engine 1045). In themethod 1100, a signal is received, in a step 1105, from a sourcescanning unit (e.g., the scanning device 935) corresponding to a codeassociated with a (physical) package. A unique PID is determined, in astep 1110, corresponding with the code. For example, the code may be orinclude the PID. The PID may, for example, be generated and/or retrieved(e.g., from the database 955) as a function of the code. Packageinformation is retrieved, in a step 1115, based on the PID (e.g., fromthe database 955). The PID is stored, in a step 1120, in a data store ofscanned packages (e.g., the code cache 1050).

If it is determined, at a decision point 1125, that scanning is notcomplete (e.g., based on a timeout period, based on absence of a signalof completion), then the method 1100 continues to monitor to receive asignal at the step 1105.

A counter variable i is initialized (e.g., to 1, as depicted) in a step1130. An ith PID is retrieved, in a step 1135, from a data store with NPIDs. In some embodiments, for example, the data store with N PIDs mayinclude the code cache 1050. In some embodiments, N may be dynamicallyupdated based on code(s) continuing to be received from one or moresource scanning units.

A display is generated (e.g., by the display device 960), in a step1140, for (at least one) master scanning unit (e.g., the scanning device965). The display corresponds to the ith PID. For example, the displaymay be a (visual) code corresponding to the ith PID in a (predetermined)schema that the master scanning unit is configured to recognize. Thecode may, by way of example and not limitation, be determined and/orgenerated by the code generation engine 1045.

If it is determined, at a decision point 1145, that all the PIDs havebeen processed (e.g., i=N, as depicted), then the method 1100 ends.Otherwise, the counter is incremented in a step 1150, and the method1100 loops back to the step 1135.

Although various embodiments have been described with reference to thefigures, other embodiments are possible.

Although an exemplary system has been described with reference to thefigures, other implementations may be deployed in other industrial,scientific, medical, commercial, and/or residential applications.

For example, in some embodiments, the scanning device 935 may, forexample, include a camera. For example, the camera may provide a(digital) image of the package (e.g., a label of the package, a portionof the package, a view of the entire package) to the code cache 1050 asthe ‘code’. The image may be stored and/or analyzed by multiple systemsvia display of the image(s) (e.g., on the display device 960) andoptical analysis of the display (e.g., by the scanning device 965). Insome embodiments, for example, a single scan (e.g., by the scanningdevice 935) may be stored and different portions may be provided todifferent masters via one or more displays. For example, a barcode fromthe package may be provided to one system. A physical display of thepackage may be provided to another system. In some embodiments, forexample, the objects scanned may be other than packages (e.g.,manufacturing items).

Some embodiments may, for example, implement a multi-master scannersystem in which source scanners provide input to at least two systems.The at least two systems may, for example, include a first scanningsystem connected to a first network, and a second scanning systemconnected to a second network. The source scanners may, for example, beconnected to the first network. The source scanner may (optically) scana physical object (e.g., a package label) and generate a first commandsignal corresponding to an attribute of the physical object (e.g., abarcode of a package). The first command signal may be transmitted viathe first network (e.g., to an internal database and/or processingcomputer). In response to the first command signal, a display unit onthe first network may generate a display corresponding to the attribute(e.g., create a display of a barcode).

The second scanning system may generate a second command signal inresponse to detecting the generated display. The second command signalmay correspond to the attribute (e.g., the barcode). The second commandsignal may be transmitted via the second network (e.g., to a proprietaryvendor database). Accordingly, a single scanning operation mayadvantageously be used to provide input to multiple master systems.

In some implementations, a queuing system may simultaneously receivemultiple first command signals (e.g., from multiple source scanners) andgenerate corresponding displays sequentially (e.g., to a single secondscanner system).

In an exemplary implementation, the source scanners may, for example,provide information to a first master system for customs import document(e.g., pediments). The source scanners may, for example, provideinformation to a second master system for shipper status information.Accordingly, with a single scanning operation, customs tracking may beupdated, and shipper tracking may be updated on separate networks and/orphysically isolated systems.

Some embodiments may, for example, provide a reconciliation system that‘squares’ data between two systems. For example, if a number of packagesphysically scanned does not reconcile with shipper tracking systems(e.g., the second network), an interface may be generated to compare thesource(s) to the physical scanning. Automatic reconciliation entries maybe generated and stored based on a comparison of the actual scan data tothe shipper system, for example. In some embodiments, by way of exampleand not limitation, reconciliation may occur after a physical package isscanned by a first scanner (e.g., a source scanner) and beforecorresponding codes are generated and displayed to at least one masterscanner (e.g., a scanner connected to an origination facility).Accordingly, for example, discrepancies may be advantageously identifiedand rectified before data is scanned into a verification system.

Some embodiments may, for example, include a risk analysis method havingnatural language processing (NLP) of manifests and a machine learningmodel to identify package contents based on NLP of the manifests. NLPmay be applied to the manifests to extract package attributes, which mayinclude attributes of the contents of the package. The model may beapplied to the extracted attributes, in combination with shipperinformation (e.g., permits) and destination information to determinewhether a package meets destination jurisdiction requirements (e.g.,import requirements). If the package is determined, within apredetermined criterion (e.g., confidence interval), to meet destinationjurisdiction requirements, then the package may be accepted as being(‘virtually’) inspected and passing inspection. If the package is notdetermined to meet destination jurisdiction requirements within thepredetermined criterion, then the package may be selected for further(e.g., manual, visual, imaging) inspection. If the package is determinedto not meet the destination jurisdiction requirements within a (second)predetermined criterion, the package may be rejected as being inspectedand failing. Accordingly, a number of packages to be physically openedmay advantageously be drastically reduced.

Each scan of a package may, for example, induce generation of a visualdisplay. For example, the visual display may display content attributes.The display may generate a visual indicia of inspection status (e.g.,pass, inspect further, fail).

Results of further inspection may, for example, be recorded. The modelmay be updated based on the results of further inspection. Accordingly,the model may be dynamically updated to increase accuracy and/orcapability (e.g., more products, more languages, more descriptors) ofthe model.

In various embodiments, some bypass circuits implementations may becontrolled in response to signals from analog or digital components,which may be discrete, integrated, or a combination of each. Someembodiments may include programmed, programmable devices, or somecombination thereof (e.g., PLAs, PLDs, ASICs, microcontroller,microprocessor), and may include one or more data stores (e.g., cell,register, block, page) that provide single or multi-level digital datastorage capability, and which may be volatile, non-volatile, or somecombination thereof. Some control functions may be implemented inhardware, software, firmware, or a combination of any of them.

Computer program products may contain a set of instructions that, whenexecuted by a processor device, cause the processor to performprescribed functions. These functions may be performed in conjunctionwith controlled devices in operable communication with the processor.Computer program products, which may include software, may be stored ina data store tangibly embedded on a storage medium, such as anelectronic, magnetic, or rotating storage device, and may be fixed orremovable (e.g., hard disk, floppy disk, thumb drive, CD, DVD).

Although an example of a system, which may be portable, has beendescribed with reference to the above figures, other implementations maybe deployed in other processing applications, such as desktop andnetworked environments.

Temporary auxiliary energy inputs may be received, for example, fromchargeable or single use batteries, which may enable use in portable orremote applications. Some embodiments may operate with other DC voltagesources, such as a 9V (nominal) battery, for example. Alternatingcurrent (AC) inputs, which may be provided, for example from a 50/60 Hzpower port, or from a portable electric generator, may be received via arectifier and appropriate scaling. Provision for AC (e.g., sine wave,square wave, triangular wave) inputs may include a line frequencytransformer to provide voltage step-up, voltage step-down, and/orisolation.

Although particular features of an architecture have been described,other features may be incorporated to improve performance. For example,caching (e.g., L1, L2, . . . ) techniques may be used. Random accessmemory may be included, for example, to provide scratch pad memory andor to load executable code or parameter information stored for useduring runtime operations. Other hardware and software may be providedto perform operations, such as network or other communications using oneor more protocols, wireless (e.g., infrared) communications, storedoperational energy and power supplies (e.g., batteries), switchingand/or linear power supply circuits, software maintenance (e.g.,self-test, upgrades), and the like. One or more communication interfacesmay be provided in support of data storage and related operations.

Some systems may be implemented as a computer system that can be usedwith various implementations. For example, various implementations mayinclude digital circuitry, analog circuitry, computer hardware,firmware, software, or combinations thereof. Apparatus can beimplemented in a computer program product tangibly embodied in aninformation carrier, e.g., in a machine-readable storage device, forexecution by a programmable processor; and methods can be performed by aprogrammable processor executing a program of instructions to performfunctions of various embodiments by operating on input data andgenerating an output. Various embodiments can be implementedadvantageously in one or more computer programs that are executable on aprogrammable system including at least one programmable processorcoupled to receive data and instructions from, and to transmit data andinstructions to, a data storage system, at least one input device,and/or at least one output device. A computer program is a set ofinstructions that can be used, directly or indirectly, in a computer toperform a certain activity or bring about a certain result. A computerprogram can be written in any form of programming language, includingcompiled or interpreted languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, which may include a single processor or one of multipleprocessors of any kind of computer. Generally, a processor will receiveinstructions and data from a read-only memory or a random-access memoryor both. The essential elements of a computer are a processor forexecuting instructions and one or more memories for storing instructionsand data. Generally, a computer will also include, or be operativelycoupled to communicate with, one or more mass storage devices forstoring data files; such devices include magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; andoptical disks. Storage devices suitable for tangibly embodying computerprogram instructions and data include all forms of non-volatile memory,including, by way of example, semiconductor memory devices, such asEPROM, EEPROM, and flash memory devices; magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, ASICs (application-specificintegrated circuits).

In some implementations, each system may be programmed with the same orsimilar information and/or initialized with substantially identicalinformation stored in volatile and/or non-volatile memory. For example,one data interface may be configured to perform auto configuration, autodownload, and/or auto update functions when coupled to an appropriatehost device, such as a desktop computer or a server.

In some implementations, one or more user-interface features may becustom configured to perform specific functions. Various embodiments maybe implemented in a computer system that includes a graphical userinterface and/or an Internet browser. To provide for interaction with auser, some implementations may be implemented on a computer having adisplay device, such as a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor for displaying information to the user, a keyboard, anda pointing device, such as a mouse or a trackball by which the user canprovide input to the computer.

In various implementations, the system may communicate using suitablecommunication methods, equipment, and techniques. For example, thesystem may communicate with compatible devices (e.g., devices capable oftransferring data to and/or from the system) using point-to-pointcommunication in which a message is transported directly from the sourceto the receiver over a dedicated physical link (e.g., fiber optic link,point-to-point wiring, daisy-chain). The components of the system mayexchange information by any form or medium of analog or digital datacommunication, including packet-based messages on a communicationnetwork. Examples of communication networks include, e.g., a LAN (localarea network), a WAN (wide area network), MAN (metropolitan areanetwork), wireless and/or optical networks, the computers and networksforming the Internet, or some combination thereof. Other implementationsmay transport messages by broadcasting to all or substantially alldevices that are coupled together by a communication network, forexample, by using omni-directional radio frequency (RF) signals. Stillother implementations may transport messages characterized by highdirectivity, such as RF signals transmitted using directional (i.e.,narrow beam) antennas or infrared signals that may optionally be usedwith focusing optics. Still other implementations are possible usingappropriate interfaces and protocols such as, by way of example and notintended to be limiting, USB 2.0, Firewire, ATA/IDE, RS-232, RS-422,RS-485, 802.11 a/b/g, Wi-Fi, Ethernet, IrDA, FDDI (fiber distributeddata interface), token-ring networks, multiplexing techniques based onfrequency, time, or code division, or some combination thereof. Someimplementations may optionally incorporate features such as errorchecking and correction (ECC) for data integrity, or security measures,such as encryption (e.g., WEP) and password protection.

In various embodiments, the computer system may include Internet ofThings (IoT) devices. IoT devices may include objects embedded withelectronics, software, sensors, actuators, and network connectivitywhich enable these objects to collect and exchange data. IoT devices maybe in-use with wired or wireless devices by sending data through aninterface to another device. IoT devices may collect useful data andthen autonomously flow the data between other devices.

Various examples of modules may be implemented using circuitry,including various electronic hardware. By way of example and notlimitation, the hardware may include transistors, resistors, capacitors,switches, integrated circuits, other modules, or some combinationthereof. In various examples, the modules may include analog logic,digital logic, discrete components, traces and/or memory circuitsfabricated on a silicon substrate including various integrated circuits(e.g., FPGAs, ASICs), or some combination thereof. In some embodiments,the module(s) may involve execution of preprogrammed instructions,software executed by a processor, or some combination thereof. Forexample, various modules may involve both hardware and software.

In an illustrative aspect, a computer program product (CPP) may includea program of instructions tangibly embodied on a non-transitory computerreadable medium wherein, when the instructions are executed on aprocessor, the processor causes operations to be performed toautomatically inspect contents of closed packages. The operations mayinclude apply, in response to receiving a signal corresponding to textthat indicates contents of a package, a natural language processingmodel to generate a content description profile, wherein the signalfurther indicates an originator of the package and a destinationjurisdiction. The operations may include apply a contentcharacterization model to the content description profile to generate acontent attributes profile associating the contents of the package withat least one predetermined content attribute. The operations may includegenerate a jurisdiction entry compliance indicator (JECI) by a secondset of operations. The second set of operations may include determinepredetermined permissions rules as a function of the destinationjurisdiction and the content attributes profile. The second set ofoperations may include determine predetermined permission attributes asa function of the originator of the package, the content attributesprofile, and the predetermined permission attributes. The second set ofoperations may include apply the predetermined permissions rules to thepredetermined permission attributes to generate the JECI that indicatesa permissibility of the contents of the package to enter the destinationjurisdiction. The second set of operations may include apply apredetermined confidence criterion to the JECI. The second set ofoperations may include, when the JECI meets the predetermined confidencecriterion, then generate an indication to pass the package withoutmanual inspection of the contents. The content characterization modelmay be dynamically trained based on historical content attributesassociated with packages corresponding to a JECI that did not meet thepredetermined confidence criterion.

Determine a predetermined permissions rules and determine predeterminedpermission attributes may include determining that the package qualifiesfor a simplified entry process.

The operations of the CPP may further include determine, based on thesignal corresponding to text, whether the package corresponds to asimplified inspection process. The operations may include When thepackage does not correspond to the simplified inspection process, thendetermine at least one of the predetermined permission attributes todefine a permit required by the destination jurisdiction as a functionof the at least one predetermined content attribute.

The JECI may be determined to not meet the predetermined confidencecriterion if any permit required is not associated with at least onecorresponding predetermined permit record stored in a database inassociation with the originator of the package.

The operations of the CPP may further include, when the JECI is betweenthe predetermined confidence criterion and a second predeterminedconfidence criterion, then generate an indication to manually inspectthe package. The operations of the CPP may further include, when theJECI is below a second predetermined confidence criterion, then generatean indication to reject the package without manual inspection of thecontents.

The operations of the CPP may further include, generate a human-readabledisplay based on the indication to pass the package. The human-readabledisplay may include a predetermined visual indicia prompting a user topass the package.

Apply a content characterization model may include model trainingoperations. The model training operations may include receive an initialcontent characterization model. The model training operations mayinclude retrieve training data. The training data may include historiccontent description data and historic content attribute data associatedwith the historic content description data. The model trainingoperations may include divide the training data into a training set anda test set. The model training operations may include train the initialcontent characterization model with the training data to generate atrained content characterization model. The model training operationsmay include apply the trained content characterization model to thehistoric content description data of the test set to generate predictedcontent attributes. The model training operations may include comparethe predicted content attributes to the historic content attribute dataof the test set. The model training operations may include, when thepredicted content attributes are not within a predetermined matchingcriterion, then receive additional training data and repeat at least thesteps of train, apply, and compare.

In an illustrative aspect, a computer-implemented method may beperformed by at least one processor to automatically inspect contents ofclosed packages. The method may include apply, in response to receivinga signal corresponding to text that indicates contents of a package, anatural language processing model to generate a content descriptionprofile. The signal may further indicate an originator of the packageand a destination jurisdiction. The method may include apply a contentcharacterization model to the content description profile to generate acontent attributes profile associating the contents of the package withat least one predetermined content attribute. The method may includegenerate a jurisdiction entry compliance indicator (JECI) by a secondset of operations.

The second set of operations may include determine predeterminedpermissions rules as a function of the destination jurisdiction and thecontent attributes profile. The second set of operations may includedetermine predetermined permission attributes as a function of theoriginator of the package, the content attributes profile, and thepredetermined permissions rules. The second set of operations mayinclude apply the predetermined permissions rules to the predeterminedpermission attributes to generate the JECI that indicates apermissibility of the contents of the package to enter the destinationjurisdiction. The second set of operations may include apply apredetermined confidence criterion to the JECI. The second set ofoperations may include, when the JECI meets the predetermined confidencecriterion, then generate an indication to pass the package withoutmanual inspection of the contents.

Determine a predetermined permissions rules and determine predeterminedpermission attributes may include determining that the package qualifiesfor a simplified entry process.

The method may include determine, based on the signal corresponding totext, whether the package corresponds to a simplified inspectionprocess. The method may include, when the package does not correspond tothe simplified inspection process, then determine at least one of thepredetermined permission attributes to define a permit required by thedestination jurisdiction as a function of the at least one predeterminedcontent attribute.

The JECI may be determined to not meet the predetermined confidencecriterion if any permit required is not associated with at least onecorresponding predetermined permit record stored in a database inassociation with the originator of the package.

The content characterization model may be dynamically trained based onhistorical content attributes associated with packages corresponding toa JECI that did not meet the predetermined confidence criterion.

The method may include, when the JECI is between the predeterminedconfidence criterion and a second predetermined confidence criterion,then generate an indication to manually inspect the package. The methodmay include, when the JECI is below a second predetermined confidencecriterion, then generate an indication to reject the package withoutmanual inspection of the contents.

Apply a content characterization model may include model trainingoperations. The model training operations may include receive an initialcontent characterization model. The model training operations mayinclude retrieve training data. The training data may include historiccontent description data and historic content attribute data associatedwith the historic content description data. The model trainingoperations may include divide the training data into a training set anda test set. The model training operations may include train the initialcontent characterization model with the training data to generate atrained content characterization model. The model training operationsmay include apply the trained content characterization model to thehistoric content description data of the test set to generate predictedcontent attributes. The model training operations may include comparethe predicted content attributes to the historic content attribute dataof the test set. The model training operations may include, when thepredicted content attributes are not within a predetermined matchingcriterion, then receive additional training data and repeat at least thesteps of train, apply, and compare.

The method may include generate a human-readable display based on theindication to pass the package. The human-readable display may include apredetermined visual indicia prompting a user to pass the package. Thepredetermined visual indicia may include a region of a predeterminedcolor corresponding to the indication to pass the package.

In an illustrative aspect, a system may include a non-transitory datastore including a program of instructions; and, a processor operablycoupled to the data store such that, when the processor executes theprogram of instructions, the processor causes operations to be performedto automatically inspect contents of closed packages. The operations mayinclude apply, in response to receiving a signal corresponding to textthat indicates contents of a package, a natural language processingmodel to generate a content description profile. The signal may furtherindicate an originator of the package and a destination jurisdiction.The operations may include apply a content characterization model to thecontent description profile to generate a content attributes profileassociating the contents of the package with at least one predeterminedcontent attribute. The operations may include generate a jurisdictionentry compliance indicator (JECI) by a second set of operations.

The second set of operations may include determine predeterminedpermissions rules as a function of the destination jurisdiction and thecontent attributes profile. The second set of operations may includedetermine predetermined permission attributes as a function of theoriginator of the package, the content attributes profile, and thepredetermined permission attributes. The second set of operations mayinclude apply the predetermined permissions rules to the predeterminedpermission attributes to generate the JECI that indicates apermissibility of the contents of the package to enter the destinationjurisdiction. The second set of operations may include apply apredetermined confidence criterion to the JECI. The second set ofoperations may include, when the JECI meets the predetermined confidencecriterion, then generate an indication to pass the package withoutmanual inspection of the contents.

The content characterization model may be dynamically trained based onhistorical content attributes associated with packages corresponding toa JECI that did not meet the predetermined confidence criterion.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made. For example,advantageous results may be achieved if the steps of the disclosedtechniques were performed in a different sequence, or if components ofthe disclosed systems were combined in a different manner, or if thecomponents were supplemented with other components. Accordingly, otherimplementations are contemplated within the scope of the followingclaims.

1. A computer program product comprising: a program of instructionstangibly embodied on a non-transitory computer readable medium wherein,when the instructions are executed on a processor, the processor causesoperations to be performed to automatically inspect contents of closedpackages, the operations comprising: apply, in response to receiving asignal corresponding to text that indicates contents of a package, anatural language processing model to generate a content descriptionprofile, wherein the signal further indicates an originator of thepackage and a destination jurisdiction; apply a content characterizationmodel to the content description profile to generate a contentattributes profile associating the contents of the package with at leastone predetermined content attribute; and, generate a jurisdiction entrycompliance indicator (JECI) by a second set of operations comprising:determine predetermined permissions rules as a function of thedestination jurisdiction and the content attributes profile; determinepredetermined permission attributes as a function of the originator ofthe package, the content attributes profile, and the predeterminedpermission attributes; apply the predetermined permissions rules to thepredetermined permission attributes to generate the JECI that indicatesa permissibility of the contents of the package to enter the destinationjurisdiction; apply at least one predetermined confidence criterion tothe JECI; and, when the JECI meets the at least one predeterminedconfidence criterion, then transmit an indication to customs broker notto perform, wherein the content characterization model is dynamicallytrained based on historical content attributes associated with packagescorresponding to a JECI that did not meet the at least one predeterminedconfidence criterion, and the at least one predetermined criterioncorresponds to accuracy of the content attribute and to conformance tothe predetermined permissions rules and the predetermined permissionsattributes.
 2. The computer program product of claim 1, whereindetermine a predetermined permissions rules and determine predeterminedpermission attributes comprises determining that the package qualifiesfor a simplified entry process.
 3. The computer program product of claim1, further comprising: determine, based on the signal corresponding totext, whether the package corresponds to a simplified inspectionprocess; and, when the package does not correspond to the simplifiedinspection process, then determine at least one of the predeterminedpermission attributes to define a permit required by the destinationjurisdiction as a function of the at least one predetermined contentattribute.
 4. The computer program product of claim 3, wherein: the JECIis determined to not meet the at least one predetermined confidencecriterion if any permit required is not associated with at least onecorresponding predetermined permit record stored in a database inassociation with the originator of the package.
 5. The computer programproduct of claim 1, further comprising: when the JECI is between the atleast one predetermined confidence criterion and a second predeterminedconfidence criterion, then generate an indication to manually inspectthe package.
 6. The computer program product of claim 1, furthercomprising: when the JECI is below a second predetermined confidencecriterion, then generate an indication to reject the package withoutmanual inspection of the contents.
 7. The computer program product ofclaim 1, further comprising: generate a human-readable display based onthe indication to pass the package, wherein the human-readable displaycomprises a predetermined visual indicia prompting a user to pass thepackage.
 8. The computer program product of claim 1, wherein apply acontent characterization model comprises model training operations, themodel training operations comprising: receive an initial contentcharacterization model; retrieve training data comprising historiccontent description data and historic content attribute data associatedwith the historic content description data; divide the training datainto a training set and a test set; train the initial contentcharacterization model with the training data to generate a trainedcontent characterization model; apply the trained contentcharacterization model to the historic content description data of thetest set to generate predicted content attributes; compare the predictedcontent attributes to the historic content attribute data of the testset; and, when the predicted content attributes are not within apredetermined matching criterion, then receive additional training dataand repeat at least the steps of train, apply, and compare.
 9. Acomputer-implemented method performed by at least one processor toautomatically inspect contents of closed packages, the methodcomprising: apply, in response to receiving a signal corresponding totext that indicates contents of a package, a natural language processingmodel to generate a content description profile, wherein the signalfurther indicates an originator of the package and a destinationjurisdiction; apply a content characterization model to the contentdescription profile to generate a content attributes profile associatingthe contents of the package with at least one predetermined contentattribute; and, generate a jurisdiction entry compliance indicator(JECI) by a second set of operations comprising: determine predeterminedpermissions rules as a function of the destination jurisdiction and thecontent attributes profile; determine predetermined permissionattributes as a function of the originator of the package, the contentattributes profile, and the predetermined permissions rules; apply thepredetermined permissions rules to the predetermined permissionattributes to generate the JECI that indicates a permissibility of thecontents of the package to enter the destination jurisdiction; apply atleast one predetermined confidence criterion to the JECI; and, when theJECI meets the at least one predetermined confidence criterion, thentransmit an indication to a customs broker not to perform manualinspection of the contents, wherein the at least one predeterminedconfidence criterion corresponds to an accuracy of the content attributeand a conformance to the predetermined permissions rules and thepredetermined permissions attributes.
 10. The computer-implementedmethod of claim 9, wherein determine a predetermined permissions rulesand determine predetermined permission attributes comprises determiningthat the package qualifies for a simplified entry process.
 11. Thecomputer-implemented method of claim 9, further comprising: determine,based on the signal corresponding to text, whether the packagecorresponds to a simplified inspection process; and, when the packagedoes not correspond to the simplified inspection process, then determineat least one of the predetermined permission attributes to define apermit required by the destination jurisdiction as a function of the atleast one predetermined content attribute.
 12. The computer-implementedmethod of claim 11, wherein: the JECI is determined to not meet the atleast one predetermined confidence criterion if any permit required isnot associated with at least one corresponding predetermined permitrecord stored in a database in association with the originator of thepackage.
 13. The computer-implemented method of claim 9, wherein thecontent characterization model is dynamically trained based onhistorical content attributes associated with packages corresponding toa JECI that did not meet the at least one predetermined confidencecriterion.
 14. The computer-implemented method of claim 9, furthercomprising: when the JECI is between the at least one predeterminedconfidence criterion and a second predetermined confidence criterion,then generate an indication to manually inspect the package.
 15. Thecomputer-implemented method of claim 9, further comprising: when theJECI is below a second predetermined confidence criterion, then generatean indication to reject the package without manual inspection of thecontents.
 16. The computer-implemented method of claim 9, wherein applya content characterization model comprises model training operations,the model training operations comprising: receive an initial contentcharacterization model; retrieve training data comprising historiccontent description data and historic content attribute data associatedwith the historic content description data; divide the training datainto a training set and a test set; train the initial contentcharacterization model with the training data to generate a trainedcontent characterization model; apply the trained contentcharacterization model to the historic content description data of thetest set to generate predicted content attributes; compare the predictedcontent attributes to the historic content attribute data of the testset; and, when the predicted content attributes are not within apredetermined matching criterion, then receive additional training dataand repeat at least the steps of train, apply, and compare.
 17. Thecomputer-implemented method of claim 9, further comprising: generate ahuman-readable display based on the indication to pass the package,wherein the human-readable display comprises a predetermined visualindicia prompting a user to pass the package.
 18. Thecomputer-implemented method of claim 17, wherein the predeterminedvisual indicia comprises a region of a predetermined color correspondingto the indication to pass the package.
 19. A system comprising: anon-transitory data store comprising a program of instructions; and, aprocessor operably coupled to the data store such that, when theprocessor executes the program of instructions, the processor causesoperations to be performed to automatically inspect contents of closedpackages, the operations comprising: apply, in response to receiving asignal corresponding to text that indicates contents of a package, anatural language processing model to generate a content descriptionprofile, wherein the signal further indicates an originator of thepackage and a destination jurisdiction; apply a content characterizationmodel to the content description profile to generate a contentattributes profile associating the contents of the package with at leastone predetermined content attribute; and, generate a jurisdiction entrycompliance indicator (JECI) by a second set of operations comprising:determine predetermined permissions rules as a function of thedestination jurisdiction and the content attributes profile; determinepredetermined permission attributes as a function of the originator ofthe package, the content attributes profile, and the predeterminedpermission attributes; apply the predetermined permissions rules to thepredetermined permission attributes to generate the JECI that indicatesa permissibility of the contents of the package to enter the destinationjurisdiction; apply at least one predetermined confidence criterion tothe JECI; and, when the JECI meets the at least one predeterminedconfidence criterion, then-transmit an indication to a customs brokernot to perform manual inspection of the contents. wherein the at leastone predetermined confidence criterion corresponds to an accuracy of thecontent attribute and a conformance to the predetermined permissionsrules and the predetermined permissions attributes.
 20. The system ofclaim 19, wherein the content characterization model is dynamicallytrained based on historical content attributes associated with packagescorresponding to a JECI that did not meet the at least one predeterminedconfidence criterion.